To tackle the first problem, one may optimize DL systems by taking
performance and complexity into account jointly. An alternative
solution is to build a new learning paradigm of low carbon footprint
from scratch. For the latter, since it targets at green ML systems by
design, it is called green learning (GL). The early development of GL
was initiated by an effort to understand the operation of computational
neurons of CNNs in [1]}, [2]}, [3]}, [4]}. Through a sequence of investigations, building
blocks of GL have been gradually developed, and more applications have
been demonstrated in recent years. As to the second problem, a clear
and logical description of the decision-making process is emphasized in
the development of GL. GL adopts a modularized design. Each module is
statistically rooted with local optimization. GL avoids end-to-end
global optimization for logical transparency and computational
efficiency. On the other hand, GL exploits ensembles heavily in its
learning system to boost the overall decision performance. GL yields
probabilistic ML models that allow trust and risk assessment with
certain performance guarantees.
